As semiconductor geometries continue to shrink, manufacturers have increasingly turned to optical techniques to perform non-destructive inspection and analysis of semiconductor wafers. Techniques of this type, known generally as optical metrology, operate by illuminating a sample with an incident field (typically referred to as a probe beam) and then detecting and analyzing the reflected energy. Ellipsometry and reflectometry are two examples of commonly used optical techniques. For the specific case of ellipsometry, changes in the polarization state of the probe beam are analyzed. Reflectometry is similar, except that changes in intensity are analyzed. Ellipsometry and reflectometry are effective methods for measuring a wide range of attributes including information about thickness, crystallinity, composition and refractive index. The structural details of ellipsometers are more fully described in U.S. Pat. Nos. 5,910,842 and 5,798,837 both of which are incorporated in this document by reference.
As shown in FIG. 1, a typical ellipsometer or reflectometer includes an illumination source that creates a mono or polychromatic probe beam. The probe beam is focused by one or more lenses to create an illumination spot on the surface of the sample under test. A second lens (or lenses) images the illumination spot (or a portion of the illumination spot) to a detector. The detector captures (or otherwise processes) the received image. A processor analyzes the data collected by the detector. The structural details of ellipsometers are described more fully in U.S. Pat. Nos. 5,910,842 and 5,798,837 both of which are incorporated in this document by reference.
Scatterometry is a specific type of optical metrology that is used when the structural geometry of a sample creates diffraction (optical scattering) of the incoming probe beam. Scatterometry systems analyze diffraction to deduce details of the structures that cause the diffraction to occur. Various optical techniques have been used to perform optical scatterometry. These include broadband spectroscopy (U.S. Pat. Nos. 5,607,800; 5,867,276 and 5,963,329), spectral ellipsometry (U.S. Pat. No. 5,739,909) single-wavelength optical scattering (U.S. Pat. No. 5,889,593), and spectral and single-wavelength beam profile reflectance and beam profile ellipsometry (U.S. Pat. No. 6,429,943). Scatterometry, in these cases generally refers to optical responses in the form of diffraction orders produced by period structures, that is, gratings on the wafer. In addition it may be possible to employ any of these measurement technologies, e.g., single-wavelength laser BPR or BPE, to obtain critical dimension (CD) measurements on non periodic structures, such as isolated lines or isolated vias and mesas. The above cited patents and patent applications, along with PCT Application WO 03/009063, U.S. Application 2002/0158193, U.S. Application 2003/0147086, U.S. Application 2001/0051856 A1, and PCT Application WO 01/97280 are all incorporated herein by reference.
Most scatterometry systems use a modeling approach to transform scatterometry results into critical dimension measurements. For this type of approach, a theoretical model is defined for each physical structure that will be analyzed. The theoretical model predicts the empirical measurements (scatterometry signals) that scatterometry systems would record for the structure. A rigorous coupled wave theory can be used for this calculation. The theoretical results of this calculation are then compared to the measured data (typically in normalized form). To the extent the results do not match, the theoretical model is modified and the theoretical data is calculated once again and compared to the empirical measurements. This process is repeated iteratively until the correspondence between the calculated theoretical data and the empirical measurements reaches an acceptable level of fitness. At this point, the characteristics of the theoretical model and the physical structure should be very similar.
Evaluation of the theoretical models is a complex task, even for relatively simple structures. As the models become more complex (particularly as the profiles of the walls of the features become more complex) the calculations can become extremely time consuming. Even with high-speed processors, real-time evaluation of these calculations can be difficult. Analysis on a real-time basis is very desirable so that manufacturers can immediately determine when a process is not operating correctly. The need is becoming more acute as the industry moves towards integrated metrology solutions wherein the metrology hardware is integrated directly with the process hardware.
A number of approaches have been developed to overcome the calculation bottleneck associated with the analysis of scatterometry results. Many of these approaches have involved techniques for improving calculation throughput, such as parallel processing techniques. An approach of this type is described in a co-pending PCT application WO 03/009063, (incorporated herein by reference) which describes distribution of scatterometry calculations among a group of parallel processors. In the preferred embodiment, the processor configuration includes a master processor and a plurality of slave processors. The master processor handles the control and the comparison functions. The calculation of the response of the theoretical sample to the interaction with the optical probe radiation is distributed by the master processor to itself and the slave processors.
For example, where the data is taken as a function of wavelength, the calculations are distributed as a function of wavelength. Thus, a first slave processor will use Maxwell's equations to determine the expected intensity of light at selected wavelengths scattered from a given theoretical model. The other slave processors will carry out the same calculations at different wavelengths. Assuming there are five processors (one master and four slaves) and fifty wavelengths, each processor will perform ten such calculations per iteration.
Once the calculations are complete, the master processor performs the best fit comparison between each of the calculated intensities and the measured normalized intensities. Based on this fit, the master processor will modify the parameters of the model as discussed above (changing the widths or layer thickness). The master processor will then distribute the calculations for the modified model to the slave processors. This sequence is repeated until a good fit is achieved.
This distributed processing approach can also be used with multiple angle of incidence information. In this situation, the calculations at each of the different angles of incidence can be distributed to the slave processor. Techniques of this type are an effective method for reducing the time required for scatterometry calculations. At the same time, the speedup provided by parallel processing is strictly dependent on the availability (and associated cost) of multiple processors. Amdahl's law also limits the amount of speedup available by parallel processing since serial portions of the program are not improved. At the present time, neither cost nor ultimate speed improvement is a serious limitation for parallel processing techniques. As the complexity of the geometry increases, however it becomes increasingly possible that computational complexity will outstrip the use of parallel techniques alone.
Another approach is to use pre-computed libraries of predicted measurements. This type of approach is discussed in U.S. Pat. No. 6,483,580, (incorporated by reference) as well as the references cited therein. In this approach, the theoretical model is parameterized to allow the characteristics of the physical structure to be varied. The parameters are varied over a predetermined range and the theoretical result for each variation to the physical structure is calculated to define a library of solutions. When the empirical measurements are obtained, the library is searched to find the best fit.
The use of libraries speeds the analysis process by allowing theoretical results to be computed once and reused many times. At the same time, library use does not completely solve the calculation bottleneck. Construction of libraries is time consuming, requiring repeated evaluation of the same time consuming theoretical models. Process changes and other variables may require periodic library modification or replacement at the cost of still more calculations. For these reasons, libraries are expensive (in computational terms) to build and to maintain. Libraries are also necessarily limited in their resolution and can contain only a finite number of theoretical results. As a result, there are many cases where empirical measurements do not have exact library matches.
One approach for dealing with this problem is to generate additional theoretical results in real-time to augment the theoretical results already present in the library. PCT WO 02/27288, published Apr. 4, 2002 suggests a combined approach of this type where a coarse library is augmented by real-time regression. Combined approaches have typically improved accuracy, but slowed the scatterometry process as theoretical models are evaluated in real-time.
Another approach is to use a library as a starting point and generate missing results as needed. For example, U.S. Pat. No. 5,867,276 describes a system of training a library to permit linear estimations of solutions. Alternatively, the use of interpolation is described in U.S. Patent Application 2002/0038196, published Mar. 28, 2002. The use of interpolation avoids the penalty associated with generating results in real-time, but may sacrifice accuracy during the interpolation process. The latter publications are incorporated herein by reference.
For these reasons and others, there is a continuing need for faster methods for computing results for scatterometry systems. The need for faster evaluation methods will almost certainly increase as models become more detailed to more accurately reflect physical structures.